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Crosstalk between diverse synthetic protein degradation tags in Escherichia coli Nicholas C Butzin, and William H. Mather ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.7b00122 • Publication Date (Web): 01 Dec 2017 Downloaded from http://pubs.acs.org on December 2, 2017

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ACS Synthetic Biology

Crosstalk between diverse synthetic protein degradation tags in Escherichia coli Nicholas C. Butzin‡1, and William H. Mather2 1

Department of Biology and Microbiology, South Dakota State University Quantitative Biosciences, Inc.

2

‡

Corresponding author: Nicholas C. Butzin Department of Biology and Microbiology South Dakota State University 1224 Medary Ave Brookings, SD 57007 Email: [email protected] Phone: 605-688-4078 Fax: 605-688-6677 Section: Research article Keywords: synthetic biology, queueing theory, protease, crosstalk, bottleneck Running title: Crosstalk of synthetic protein degradation tags

1 ACS Paragon Plus Environment

ACS Synthetic Biology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Abstract Recently, a synthetic circuit in E. coli demonstrated that two proteins engineered with LAA tags targeted to the native protease ClpXP are susceptible to crosstalk due to competition for degradation between proteins. To understand proteolytic crosstalk beyond the single protease regime, we investigated in E. coli a set of synthetic circuits designed to probe the dynamics of existing and novel degradation tags fused to fluorescent proteins. These circuits were tested using both microplate reader and single-cell assays. We first quantified the degradation rates of each tag in isolation. We then tested if there was crosstalk between two distinguishable fluorescent proteins engineered with identical or different degradation tags. We demonstrated that proteolytic crosstalk was indeed not limited to the LAA degradation tag, but was also apparent between other diverse tags, supporting the complexity of the E. coli protein degradation system.


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Introduction Proper cell behavior is maintained using a finite pool of processing resources, such as the limited pool of enzymes required for gene transcription and protein translation1-2. Natural biological circuits are largely thought to have evolved to buffer against the effects of limited resources, but we are beginning to understand how processing machinery can form a bottleneck that is in fact leveraged as a control or signaling mechanism3-4. Proteolytic (protein degrading) pathways, in particular, have been found to form functional bottlenecks in a native E. coli network regulating the stationary phase sigma factor S (σS). The protein σS is degraded by the ClpXP proteolysis system (ClpXP protease and its chaperones) much faster during exponential growth phase5-6 than stationary phase7, and the corresponding buildup of σS in stationary phase acts as a signal triggering the stress response system for starvation8. An explanation for the increased stability of σS in stationary phase is that there are an increased number of mistranslated proteins targeted for degradation by ClpXP. Mistranslated proteins are targeted to ClpXP because they have a C-terminal SsrA tag (sometimes labeled LAA tag) due to a special transfer-messenger RNA (tmRNA) being added to mRNA to flag peptides for degradation9-10. These proteins compete for a limited number of proteases, especially ClpXP, which results in the formation of a “queue” of substrates for the protease that increases the apparent half-life of σS 8. The complexity of natural proteolytic pathways serves as a barrier to understanding this phenomenon, and so synthetic circuits offer a valuable alternative approach11. It was predicted based on the theoretical modeling of a synthetic oscillator that overexpression of LAA-tagged proteins could lead to saturation of proteolytic machinery12-13, i.e. that proteolytic machinery could be limiting. Recently, a synthetic circuit more directly demonstrated that two distinguishable fluorescent proteins engineered with ClpXP-targeting LAA-tags can lead to the formation of a queue that resulted in crosstalk, such that the buildup of one protein can increase the concentration of another (Fig. 1A)8. Queueing theory has since been adopted to describe how competition between substrates for a particular protease can lead to pronounced coupling and statistical correlation8, 14-16. The impact of proteolytic queueing competition leads to a rewiring of natural and synthetic circuits to include mutual modulation of substrate degradation rates17. This effect in much of existing bacterial synthetic biology is due to targeting multiple species of protein to a common protease ClpXP, and the effect applies to all but one existing bacterial synthetic oscillator 18. The exception is the recently modified repressilator19, where active degradation by protease was systematically removed to produce a more robust growthdependent (dilution-dependent) oscillator, which interestingly was predicted based on a prior analysis of proteolytic competition17. The single protease crosstalk picture is too simplistic for native circuits, and the reliance on a single degradation pathway for bacterial synthetic oscillators presents a scalability problem that limits the complexity of circuits that can be developed. To address this issue, we investigated the crosstalk between multiple native degradation pathways in E. coli. This study extends a prior investigation of computational models that suggested a multi-protease proteolytic bottleneck may still contribute substantially to crosstalk in simple and complex (oscillatory) networks20. The influence of crosstalk in multi-protease models was evident even between substrates with substantially different affinities for a protease. Crosstalk may then be generic and complex in native networks. In particular, the different proteolytic networks of E. coli may exhibit strong mutual crosstalk, as only three proteases (Lon, ClpXP, and ClpAP, see Table S1) together are required to account for approximately 70%–80% of ATP-dependent degradation in bacterial cells21-22. These few proteases thus establish the bulk of proteolytic degradation bandwidth that



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is shared among a diverse set of actively degraded proteins (about 20% of newly synthesized polypeptides are degraded23). This fact combined with the phenomenon of queueing coupling suggests that crosstalk between diverse cellular networks may be typical and cannot be easily relieved by the limited number of proteolytic pathways. In this work, we used a synthetic biology approach to understand crosstalk between the proteolytic systems of E. coli. We designed several synthetic genes that produce fluorescent proteins with protease-targeting tags to serve as probes for and indicators of crosstalk between different protease queues. Using the same E. coli strain, we systematically investigated these genes in isolation and in combination. The tags that resulted in substantial degradation in isolation were further investigated for proteolytic crosstalk by co-expressing fluorescent proteins with specific tags. We observed that there is often measurable crosstalk when two fluorescent proteins engineered with identical degradation tags were targeted to the same protease, which is as expected based on the queueing analogy. We also identified a range of crosstalk strengths, ranging from undetectable to high, when proteins are selectively targeted to different protease pathways. Results and Discussion Multiple synthetic amino acid sequence tags target fluorescent proteins for degradation by different proteases. To study proteolytic degradation by different proteases, fluorescent proteins were engineered with different potential degradation tags on either the N-terminus or Cterminus (Table S2). Previously tested degradation tags could not be compared directly to one another because they were characterized in different E. coli strains and under different conditions. To our knowledge, this is the first systematic investigation of multiple degradation tags in E. coli. We tested the previously determined degradation tags and several newly designed tags (Table 1A-B). Degradation tags were fused to multiple fluorescent proteins and tested for activity using a high-throughput in vivo microplate reader assay. All putative degradation tags tested targeted YFP for degradation (Fig. 2 and Fig. S1). All circuits were designed on the same genetic platform with an identical promoter, an identical plasmid, an identical start codon, and an identical ribosomal binding site (RBS). HipBc20, MazE, SoxSn20, RepA15, and HipB were identified as poor degradation tags. In all upcoming experiments, only one poor degradation tag with a medium degradation rate, HipB, was tested for proteolytic crosstalk with other tags. All other tags (LAA, RepA70, MarA, and MarAn20) except SoxS were tested for proteolytic crosstalk. The main proteases of an E. coli cell can be overloaded when proteins are designed to be targeted to a common protease via engineered degradation tags. Previous researchers utilized LAA tagged proteins to demonstrate that proteolytic queues form at ClpXP in the overloaded state8. We recreated their results using our own circuits (Fig. 3). We then explored if other degradation tags would result in the formation of queues and if queues could form with proteases other than ClpXP. YFP and CFP proteins were engineered with RepA70 tags for degradation by ClpAP. The fluorescence levels of RepA70-YFP were monitored as we increase the level of RepA70-CFP proteins by adding the chemical inducer IPTG. The fluorescence of RepA70-YFP increased as more RepA70-CFP was produced (Fig. 3C), and this indicated that ClpAP protease could be overloaded and a proteolytic-queue forms similar to what was observed with the LAA tagged proteins targeted to ClpXP8. We also tested two other tags, MarA and MarAn20 (20 amino acids from the N-terminal of MarA), which target proteins to be degraded by the Lon protease. The Lon protease was weakly overloaded by MarA and MarAn20 tagged proteins (Fig. 3D-E). This made us question if Lon could be overloaded when both MarA and MarAn20 were co-produced. Indeed, this was the case (Fig. 3F). We cannot


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exclude of course that multiple proteases contribute to the crosstalk in any one of these experiments, but we do not anticipate qualitatively different behavior in that case based on theoretical results20. The main proteases of E. coli can exhibit different levels of crosstalk depending on the degradation tags used. We have demonstrated that ClpXP, ClpAP, and Lon can be overloaded using two proteins engineered with identical degradation tags targeted for a specific protease. We hypothesized that crosstalk between proteases may occur due to finite cross affinity of tags with other proteases (Fig. 1B). To test this hypothesis in a synthetic system, we monitored the level of a fluorescent protein (YFP) targeted to one protease while producing another protein (CFP) targeted to a different protease. There was strong crosstalk when proteins with the LAA degradation tag (targeted to ClpXP) were co-produced with proteins with other tags (RepA70, MarA, MarAn20, and HipB; Fig. 4A-D). Though strong, crosstalk between LAA and these other tags was less pronounced than between LAA and itself. One hypothesis for crosstalk between tags is that these tags are likely to be targeted to multiple proteolytic pathways. For instance, although ClpXP is the primary protease that degrades LAA-tagged proteins9, 24, several other proteases may recognize this tag (Table S1). Perhaps due to the importance of removal of potentially harmful waste proteins, cells evolved to have “backup” (a cellular redundancy) proteolytic pathways to remove peptides even if ClpXP is overloaded. Overloading the ClpXP proteases with LAA tags may be more dramatic because a substantial number of newly synthesized polypeptides in the cell are degraded23 by this protease through the tmRNA quality control system (see reviews 25-26). Perhaps proteases in wild-type cells experience overloading by proteins with LAA tags more frequency than other tags and cells have evolved a more holistic response (several proteases acting to remove LAA tagged proteins) than with the other tags we tested. This conjecture has yet to be tested and there are other potential mechanisms to explain the strong crosstalk response for LAA tagged proteins compared to all other tags. Although LAA-tagged proteins are often utilized in synthetic biology circuits18, our results suggest it is not an ideal candidate for orthogonal circuits that depend on a proteolytic pathway such as most oscillators. We have tested several other degradation tags that can be potentially used in future synthetic circuits. There was measurable crosstalk when proteins with RepA70 and MarA degradation tags were co-produced (Fig. 4E), but no detectable crosstalk when MarAn20 and RepA70 tagged proteins were co-produced (Fig. 4F). This suggests that MarAn20 and RepA70 may be useful for future synthetic circuits. All proteins with the degradation tags tested thus far were rapidly degraded. We tested a medium-degradation tag, HipB, and, interestingly, we observed no apparent crosstalk between proteins with HipB-tags and all other tested tags (Fig. S2), except for the LAA tag (Fig. 4D). For all experiments, YFP derivatives were expressed using the same genetic platform with an identical promoter, an identical plasmid, an identical start codon, and an identical RBS. CFP derivatives were set up in a similar fashion. YFP encoding circuits were placed under the control of PLtetO (promoter induced by Dox) on a medium copy number plasmid (p31Cm contains p15A origin of replication), while CFP encoding circuits were placed under the control of Plac/ara (promoter induced by arabinose and IPTG) on a high copy number plasmid (p24Km contains colE1 origin of replication). YFP and CFP production are not equal in level due to the necessity of expressing them with different controllable promoters and this difference in production level was compounded by the use of plasmids with different copy numbers. The activities of these promoters have been compared in roughly the same genetic background, and the PLtetO promoter results in more than two times as much protein than the Plac/ara promoter when they are both fully induced27. CFP and YFP were used for all experiments because they are both



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monomers and have the same protein length, similar amino acids sequences (97% identical with only eight non-identical amino acids) and quite similar maturation times. Although these fluorescent proteins are similar, small differences in amino acid sequences could contribute to crosstalk. To investigate this possibility, we tested for crosstalk when a tag was placed on either fluorescent protein. For example, we first measured the crosstalk of YFP-LAA to that of CFPMarAn20. We then compared the level of crosstalk when YFP-MarAn20 to that of CFP-LAA. We did a similar swap of tags to compare proteins engineered with LAA and MarA, LAA and RepA70, and HipB and RepA70. In all cases, similar results were obtained regardless of the plasmid or promoters used in the crosstalk experiments (Fig. S7). As predicted the total level of crosstalk varied during these tag-swapping experiments; different plasmids and promoters result in a different concentration of total protein produced. These results support our findings that the main proteases of E. coli can show different levels of crosstalk based on the degradation tags used. Although all circuits were designed on the same genetic platform with only the degradation tags varied for the CFP and YFP derivatives, respectively, an unintentional physiological effect such as a change in the cell doubling-time could occur due to the production of the fluorescent proteins or the degradation tags. We first compared the doubling-time (the time the cultures took to grow from OD600nm 0.2 to 0.4; all comparisons of crosstalk were measured at an OD600nm of ~0.4) of biological replicas (n = 72) containing no Dox and no IPTG and determined the range of doubling-time to be from 98-122 min (mean doubling time of 110 ± 12 min. and %STDEV of the mean of 11%). We then focused on the two cultures, DZ89 (CFP-LAA and YFP-HipB), and DZ74 (CFP-MarAn20 and YFP-RepA70), which had mean doubling-times outside of this range. DZ89 had a mean doubling-time of 96 ± 10 min. with Dox (200 ng/ml) and 117 ± 6 min. with Dox (200 ng/ml) and 0.5 mM IPTG. DZ74 had a mean doubling-time of 92 ± 6 min. with Dox (200 ng/ml) and 91 ± 7 min. with Dox (200 ng/ml) and 0.5 mM IPTG. A Student’s t-test showed that the difference in doubling-time for these strains with and without the addition of IPTG was not apparently significant (p-value > 0.5). These results suggest that the crosstalk we observed between degradation tags is not due to a change in growth. Even though we have focused on a specific period in which the cell is growing (OD600nm 0.2 to 0.4) and the environment in which nutrients are plentiful (LB rich media) to probe our crosstalk hypothesis, it remains to be seen the effect of crosstalk on cell physiology in a more nutrient-poor or other environments. Tag-dependent crosstalk does not appear to be rooted in a growth rate effect, but we still cannot exclude based on our experimental results that complicated biochemical network dynamics are responsible for crosstalk. To this end, we tested whether the quantitative features of crosstalk could be explained by a simple model based on competition between tagged proteins for protease (see Methods)20. We used 120 data points (every value for mean YFP fluorescence in Figs. 3 and 4) to fit a common model with three proteases and a total of 25 free parameters, and we found that this model was sufficient to reproduce major trends in the data (Fig. S8). However, we caution that further investigation is required to confirm if the details of this theoretical picture are correct, since the model was sufficiently complex to fit our data for a range of parameters. Our fitting routine was thus conditioned to penalize large enzymatic degradation rates and unusually large tag-protease affinities to avoid good (low error) but unrealistic fits. This theoretical analysis at a minimum demonstrates that a major portion of tagdependent crosstalk in our data can be described by competition between tagged proteins for multiple proteolytic pathways. Single-cell data from microscopy slides support the high-throughput microplate data. Single-cell data and microplate batch data had similar results when proteins were targeted to the same proteolytic pathways, although crosstalk is more apparent with the single-cell data,


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especially for the RepA70 tag (Fig. 5A). The single-cell data and microplate batch data also had similar results when proteins were targeted to different pathways (Fig. 5B and Fig. S3). The only minor difference detected by these methods was when proteins with RepA70 and MarA were co-produced; the batch data showed weak crosstalk (crosstalk was only detected at the highest Dox concentration; Fig. 4E), while the single-cell data showed no apparent crosstalk (Fig. 5B). Both independent techniques, high-throughput microplate batch and single-cell experiments, yielded similar but as predicted not identical results. The high-throughput microplate batch data allowed us to get an overall picture of the queueing phenomena with a gradient of inducer concentrations, but it is an average measurement of 1000’s of cells. Analysis of single-cell images is an independent technique more sensitive than batch data. Single-cell analysis is less subject to the averaging effects characteristic of batch (population-scale) methods and offers a level of discrete detection that is unobtainable with traditional microbiological techniques28. Conclusion Using synthetic circuits, we demonstrated that crosstalk could arise between several different proteolytic pathways. We first characterized a collection of amino acid sequences (tags) that target proteins toward active degradation. Proteins fused to these tags were expressed in a common strain using identical promoters and identical plasmid origins, which allowed us to equitably compare the effectiveness of these tags as degradation signals. This initial characterization of molecular parts is itself of value to synthetic biology. We then co-expressed YFP and CFP with generally different tags to determine crosstalk between pathways. The LAA tag was particularly prone to exhibiting crosstalk with itself and other tags. Other tag combinations demonstrated a range of crosstalk, though not as strong as we measured between LAA and itself. Since many current synthetic systems rely on proteins engineered with LAA tags (targeted to ClpXP)12, 16, 18, 29-38, our results strongly suggest that proteolytic crosstalk may be a major hurdle to scalability, indicating that new protease tags are required. Our select pairs of degradation tags with minimal to moderate crosstalk may have future applications in this direction, e.g. in the development of the first synthetic orthogonal to semi-orthogonal oscillators. Of course, tags with strong crosstalk may still be of value, as they could be used for more coordinated modules. Great efforts have been made to design synthetic circuits that are orthogonal to each other, and the use of selective targeting proteins to different proteases may be one method to isolate circuits from one another. Fairly recently, the Collins’ lab developed a transcription-based toggle switch in E. coli utilizing a Mesoplasma florum Lon (mf-lon) protease that target pdt tagged proteins. Pdt is the SsrA-tag utilized in M. florum and this tag is a target of mf-Lon but not apparently by E. coli’s Lon or ClpXP. They demonstrated by monitoring a fluorescent protein that a synthetic toggle switch can be driven and controlled through a proteolytic pathway39. Their system could be utilized to make synthetic orthogonal oscillators. Although, removal of tagged proteins (e.g. fluorescent proteins) from their system took hours, which would limit our ability to design oscillators that function in minutes. A direct measure of potential crosstalk between pdt tagged proteins and E. coli protease tags has not been reported. However, the Collins’ system provides another avenue to produce orthogonal oscillator that may be useful to test alongside E. coli systems studied in this report. Our findings have implications for both the study of natural systems and the design of complex synthetic systems. In natural systems, cells may use transcriptional regulation and proteolytic coupling as a form of regulation based on the desired response. Transcriptional regulation allows for either a coupled or an uncoupled response. Proteolytic coupling has advantages over a transcriptional response; it may be a quicker mechanism that requires less energy. A transcriptional response to an overloaded protease requires transcription of RNA, production of protein, protein folding, and removal of excess protease after the overabundant substrates are



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removed. In contrast, proteolytic queueing utilizes other proteases already present in the cell, thus a subsequent response is quick and requires no additional energy to be effective nor to remove the protease later. This means cells can respond to misfolded proteins and the environmental conditions that cause an increase in faulty translation without significantly slowing growth. E. coli utilizes proteolytic queues for σS regulation8. Our recent stochastic models suggest that proteolytic queues may also augment other natural systems such as toxin-antitoxin mechanism used to modulate bacterial persistence20. Methods Reagents. All of the reagents were reagent grade and purchased from Sigma-Aldrich, Fisher Scientific, or Thomas Scientific unless otherwise stated. Strains and Plasmids. All strains were derived from E. coli DH5alphaZ1 (purchased from Dr. Rolf Lutz). All of the plasmid DNA sequences (in GenBank format) are provided in the Supporting Information (Zip file 1). CFP and YFP gene derivatives were cloned downstream of the PLtetO promoter of p31Cm (chloramphenicol 10 µg/ml)16 or downstream of the Plac/ara promoter of p24Km (kanamycin 25 µg/ml). The plasmid p24Km was constructed by PCR amplification and cloning of a T1 terminator into KpnI-ClaI restriction sites of pZE24MCS (purchased from Dr. Rolf Lutz). The CFP and YFP gene derivatives (Table 1; Table S2) were purchased from ThermoFisher or GenScript. The cultures were grown in Lysogeny broth (LB). Absorbance and fluorescence measurements with a microplate reader. Cells were passed (1:250 dilution) from an overnight culture or a culture stored at 4° C for less than two weeks into LB and antibiotics. Cells were grown at 37° C at a shaking rate of 250 rpm for 2-3 h, and then 100 µl cell culture was added to individual wells of a 96-Well Optical-Bottom Plates with Polymer Base (ThermoFisher) already containing 100 µl of regents (LB, antibiotics, and inducers). A Cytation 3 microplate reader (BioTek) was used to grow and monitor cells. Cell growth was measured at OD600 (Optical density at 600 nm). The excitation and emission (Ex/Em) used for YFP and CFP were 510/540 and 447/477 nm, respectively. The wavelengths for Ex/Em were empirically determined to minimize crosstalk between different fluorescent proteins. The background of the media (median absorbance and mean YFP fluorescence) was subtracted from the raw reads. The fluorescence values were compared at OD600 nm ~0.4 (mid-log growth phase), and then the fluorescent values were normalized by dividing by the OD. Four biological replicas were used to calculate the mean fluorescence and standard deviation. Bleed-through from one fluorescent channel into another was tested by two different methods. First, the potential bleed-through from the YFP channel into the CFP channel was tested by producing untagged YFP (the brightest YFP derivative) at different doxycycline (Dox) concentrations (inducing YFP) and monitoring CFP production. A similar test was done with only the untagged CFP protein (induced by isopropyl β-D-1-thiogalactopyranoside (IPTG) and arabinose). No apparent bleed-through was detected in the CFP channel when YFP was produced (Fig. S4A) and no apparent bleed-through was detected in the YFP channel when CFP was produced (Fig. S4B). Second, bleed-through was tested with E. coli strains carrying both untagged fluorescent proteins CFP and YFP. No apparent bleed-through was detected in the CFP channel when YFP was produced (Fig. S4C), and no apparent bleed-through was detected in the YFP channel when CFP was produced (Fig. S4D) despite carrying genes for both fluorescent proteins. Single-cell snapshots. Cells were harvested from microplate reader wells at mid-log growth phase (near OD600 nm 0.4), and single-cell snapshots were taken on a Nikon Ti microscope with


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CFP and YFP fluorescence cubes at 1000× magnification (a 100× objective coupled to additional 10× magnification). Phase-contrast images and fluorescence images were taken. All fluorescence images were taken with the same exposure (75 ms) and light intensity (6% solointensity) based on bleed-through tests. We empirically determined these settings had no apparent bleed-through (Fig. S5). For each single-cell fluorescent data set, the mean and SEM were calculated from 441-1133 individual cells. Analysis of Mean Fluorescence. To analyze single-cell snapshots, we used a custom pipeline for single-cell segmentation based on machine learning techniques. This process leveraged a FastRandomForest classifier trained and then applied using the Trainable Weka Segmentation tool in Fiji40. The classifier used phase contrast images (with pixel values normalized to a common mean and standard deviation) to identify individual cells that were in focus (Fig. S6), and the resulting segmented regions were saved as a separate collection of segmented images. Segmented images were then processed by custom Python 2.7 scripts using the SciPy and OpenCV packages. These scripts both measured single-cell fluorescence in corresponding fluorescence images and also computed single cell statistics. Model Fitting to Single Tag Microplate Data. Fluorescence data for the single tag constructs in microplate experiments were fit to a simple enzymatic degradation model in order to describe the data points by a mathematical function. We checked whether the measured YFP-tag fluorescence (for a range of tags ) for a given concentration could be described by the steady-state of the ODE:

= ⋅ − −

(1)

i.e., with the solution to the equation 0=⋅

− −

(2)

In this model, we have rescaled time such that the dilution rate is 1.0, and we allow the apparent enzymatic velocity and molar constant to, in principle, have independent values for each tag. The parameters and ! characterize the production rate of protein for a given level of . We assume that untagged YFP proteins are degraded with zero enzymatic velocity, i.e. "# $%%& = 0. All data points, excepting data for YFP-RepA15, were jointly fit to this model using the scipy.optimize.minimize function from the SciPy library. Parameter values for one of our best fits are reported in SI Table S3, and the fit is displayed graphically in Fig. S1. It is worth noting that if a fitted value for is large relative to typical values of , then the relevant degradation parameter is the first-order degradation rate constant, / , and only this ratio is likely very meaningful regarding model fit. Model Fitting to Dual Tag Microplate Data. An extended model was fit to strains expressing two tagged fluorescent proteins simultaneously (see Figs. 3 and 4). Our model for a strain expressing YFP with tag T1 and CFP with tag T2 assumes a finite number of proteases with simple functions for their enzymatic rates based on mutual competition20. The model for any two tag choices can be written as a two-dimensional system: (

= )* − + − ∑ - - ⋅ .+/

/0,( (

0,( ( /0,2 2

3

(3)



2

= )456* −

7

− ∑ - - ⋅ .+/

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/0,2 2

0,( ( /0,2 2

3

(4)

where + is the fluorescence for YFP-T1, 7 is the fluorescence for CFP-T2, is the YFP production rate (only dependent on [DOX]), is the CFP production rate (only dependent on [IPTG]), - is the maximum enzymatic velocity for protease P, and 8-, is the relative affinity between protease P and tag T. For our particular model, we let P range from 1 to 3 (3 independent proteases). We again rescaled time such that the dilution rate is 1.0. We estimated the steady state of this model by simulating the ODE for 100 time units, which is many fold larger than the characteristic dilution time. To fit the model to data, we jointly minimized the sum of squared differences between the steady state YFP fluorescence of the model and mean experimental YFP fluorescence (across replicates) for all 12 tag combinations and all [DOX], [IPTG] combinations in Figs. 3 and 4. We first subtracted away potential background fluorescence in experiment, which we estimated by the median YFP fluorescence across all experiments for the [DOX] = 0.0 ng/ml, [IPTG] = 0.0 mM condition. We also added terms to our error function to penalize parameter sets with unrealistically large - and 8-, , since we observed good (low error) but unrealistic fits. This minimization across all experiments allowed us to condition a model with 25 free parameters ( for 5 [DOX] values, for 2 [IPTG] values, - for 3 proteases, and 8-, for 15 protease-tag combinations), using 120 YFP mean data points. A number of potential model fits were found, but we retained a model fit with a clear underloaded to overloaded transition in the YFP-LAA vs. CFP-LAA data. Author Information ‡ Corresponding Author E-mail: [email protected]. Address: 1224 Medary Ave SDS 228 Box 2104A Brookings, SD 57007 Phone: 605-688-4078. Fax: 605-688-6677. Author Contributions N.C.B. performed all wet-lab experiments and wrote the manuscript. N.C.B. and W.H.M. analyzed microscopy images. W.H.M developed software for the analysis of single-cell images and developed all mathematical models. All authors contributed to the discussion and editing of the manuscript. Conflict of Interest The authors declare no competing financial interest. Acknowledgements Funding for this research was provided by the 454 National Science Foundation under Grant No. MCB-1330180.


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Figures and Tables Fig. 1. (A) Previous researchers have demonstrated that a queue can form through competition for degradation using fluorescent proteins (CFP and YFP) targeted (through the LAA tag) to the same protease ClpXP8. (B) We tested if two tags that are specifically targeted to two different proteases (P1, P2) could form queues. Fig. 2. Batch single degradation tag results. YFP derivatives were expressed from the PLtetO promoter using the chemical inducer doxycycline (Dox) at 200 ng/ml. A high-throughput in vivo microplate reader assay was used to determine the fluorescent level of proteins with and without degradation tags. The percentage degradation was calculated in this manner: 100% X (1 - YFPtag/YFPuntagged) and standard deviations were calculated using a Taylor expansion7. This assumed that untagged YFP is at maximum production and this avoids dividing by a small number. The percentage degradation was calculated for proteins induced at different concentrations of Dox (using data from Fig. S1). Four biological replicas were used to calculate the mean fluorescence and standard deviation from in vivo microplate reader batch data. Fig. 3. Degradation queues form when the proteins are engineered with degradation tags targeted to the same proteolytic pathway. (A) No apparent crosstalk was detected when both YFP and CFP were co-produced without degradation tags. (B) LAA-tagged proteins targeted to ClpXP had the strongest apparent crosstalk, while (C) RepA70-tagged proteins target to ClpAP had weak crosstalk compared to what was determined for LAA-tagged proteins. (D) MarA tagged proteins targeted to Lon had weak crosstalk, but (E) MarAn20-tagged (20 amino acids from the N-terminal of MarA) proteins also targeted to Lon had greater crosstalk. (F) Production of CFP-MarAn20 resulted in an increased level of YFP-MarA indicating crosstalk between these two tags targeted to the Lon protease. YFP derivatives were expressed from the PLtetO promoter using the inducer Dox, while CFP derivatives were expressed from Plac/ara promoter using inducer 0.5 mM IPTG (all experiments contained 1% arabinose). Each tag comparison is indicated by tag/tag with CFP being the first tagged and YFP being the second tagged protein. Four biological replicas were used to calculate the mean fluorescence and standard deviation from in vivo microplate reader batch data. FU: arbitrary fluorescence unit. Fig. 4. The main proteases of an E. coli cell can have different levels of proteolytic crosstalk depending on the degradation tags utilized. Strong crosstalk was detected when proteins with the LAA-tag (targeted to ClpXP) was co-produced with (A) RepA70, (B) MarA, (C) MarAn20, and (D) HipB tagged proteins. Proteins engineered with (E) RepA70 and MarA tags, and (F) MarAn20 and RepA70 tags had measurable crosstalk and no detectable crosstalk, respectively. YFP derivatives were expressed from the PLtetO promoter using the inducer Dox, while CFP derivatives were expressed from Plac/ara promoter using inducer 0.5 mM IPTG (all experiments contained 1% arabinose). Each tag comparison is indicated by tag/tag with CFP being the first tagged and YFP being the second tagged protein. Four biological replicas were used to calculate the mean fluorescence and standard deviation from in vivo microplate reader batch data. FU: arbitrary fluorescence unit. Fig. 5. Analysis of crosstalk using both single-cell and batch data. Single-cell data and microplate batch data had similar results with (A) proteins engineered with degradation tags targeted to the same protease, and (B) proteins engineered with degradation tags targeted to different proteases. Crosstalk with HipB-tagged proteins was also determined using these methods (Fig. S3). Single-cell data was acquired by imaging cells at 1000X magnification with and without IPTG (inducing CFP derivatives) using a fluorescent confocal microscope. Cells



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were identified and fluorescence levels were calculated using Fiji, machine learning, and inhouse scripts (see Methods). The percentage crosstalk was calculated in this manner: 100%⋅ (1 – YFPNo IPTG/YFP0.5 mM IPTG) with the standard deviation and SEM calculated using a Taylor expansion 7. Use of this ratio avoids dividing by a small number and reduces the impact of statistical error. Each tag comparison is indicated by tag/tag with CFP being the first tagged protein and YFP being the second tagged protein. For each single-cell fluorescent data set, the mean and SEM were calculated from 441-1133 individual cells. For in vivo microplate reader batch data, four biological replicas were used to calculate the mean fluorescence and standard deviation. YFP derivatives were expressed from the PLtetO promoter using Dox 200 ng/ml, while CFP derivatives were expressed from Plac/ara promoter using 0.5 mM IPTG (all experiments contained 1% arabinose).


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Table 1. (A) Degradation tags and (B) source (E. coli K12 MG1655). (A) Major protease Tag Tag detail target LAA Serves as the target for cellular ATPClpXP9, 24 9, 24 dependent proteases . RepA70 A high-affinity substrate of ClpAP that is ClpAP43 degraded poorly by ClpXP41.The N-terminus of RepA is required for recognition by ClpAP42. RepA15 The N-terminus of RepA is required for ClpAP43 42, 44 recognition by ClpAP . Adding the adapter KLAAALE results in increased degradation45. SoxS The amino-terminus of SoxS appears to be Lon46 important for proteolytic degradation of SoxS46. SoxSn20 This study. Lon MarA The amino-terminus of MarA appears to be Lon46 the target of Lon46. MarAn20 This study. Lon MazE ClpAP degrades MazE47. Lon also degrades ClpAP47 MazE under amino acid starvation48. HipB Fast degradation of HipB is dependent on the Lon48-50 presence of Lon49. When HipB is bound to HipA the DNA-bound form of HipB forms a compact dimer, but the last 16 amino acid residues are disordered. This disordered part of HipB functions has been suggested as a recognition site for Lon50. HipBc20 This study. Lon

Minor protease target ClpAP, Lon, FtsH, Tsp (Prc)9 ClpXP43

ClpXP43 FtsH46

Lon48

(B) Protein Function SoxS A superoxide response regulon transcriptional activator. SoxS induces sox regulon when superoxide levels increase51. MarA A multiple antibiotic resistance transcriptional regulator52. MazE The antitoxin that inhibits the toxin MazF53. HipB The antitoxin that inhibits the toxin HipA54. RepA A plasmid P1 initiator protein55.

Locus tag b4062 b1531 b2783 b1508 Plasmid origin



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Supporting Information Supplementary Table S1. Key cytoplasmic proteases and chaperones. Supplementary Table S2. Plasmids and strains. Supplementary Table S3. Extracted parameters for single tag fluorescence data. Supplementary Table S4. Parameters used for the dual tag model fit in Fig. S8. Supplementary Fig. S1. Single degradation tag results with different levels of Dox. Supplementary Fig. S2. Proteins engineered with the HipB tag and co-produced with other tagged proteins. Supplementary Fig. S3. Analysis of crosstalk using both single-cell and batch data with HipBtagged proteins. Supplementary Fig. S4. Test for bleed-through from one channel to another in in vivo microplate reader experiments. Supplementary Fig. S5. Test for bleed-through from one channel to another in single-cell snapshot experiments. Supplementary Fig. S6. Method for Single Cell Analysis. Supplementary Fig. S7. Swapping degradation tags lead to similar levels of crosstalk between proteases. Supplementary Fig. S8. Fit of a Model to Dual Tag Data. Supplementary Zip file 1. DNA sequences for the constructs used. References 1. Scott, M.; Klumpp, S.; Mateescu, E. M.; Hwa, T., Emergence of robust growth laws from optimal regulation of ribosome synthesis. Molecular systems biology 2014, 10 (8). 2. Weisse, A. Y.; Oyarzun, D. A.; Danos, V.; Swain, P. S., Mechanistic links between cellular trade-offs, gene expression, and growth. Proceedings of the National Academy of Sciences of the United States of America 2015, 112 (9), E1038-47. 3. Rowland, M. A.; Fontana, W.; Deeds, E. J., Crosstalk and Competition in Signaling Networks. Biophys J 2012, 103 (11), 2389-2398. 4. Rowland, M. A.; Deeds, E. J., Crosstalk and the evolution of specificity in two-component signaling (vol 111, pg 5550, 2014). Proceedings of the National Academy of Sciences of the United States of America 2014, 111 (25), 9325-9325. 5. Flynn, J. M.; Neher, S. B.; Kim, Y. I.; Sauer, R. T.; Baker, T. A., Proteomic discovery of cellular substrates of the ClpXP protease reveals five classes of ClpX-recognition signals. Molecular cell 2003, 11 (3), 671-83. 6. Schweder, T.; Lee, K. H.; Lomovskaya, O.; Matin, A., Regulation of Escherichia coli starvation sigma factor (sigma s) by ClpXP protease. Journal of bacteriology 1996, 178 (2), 4706. 7. Zhou, Y.; Gottesman, S., Regulation of proteolysis of the stationary-phase sigma factor RpoS. Journal of bacteriology 1998, 180 (5), 1154-8. 8. Cookson, N. A.; Mather, W. H.; Danino, T.; Mondragon-Palomino, O.; Williams, R. J.; Tsimring, L. S.; Hasty, J., Queueing up for enzymatic processing: correlated signaling through coupled degradation. Molecular systems biology 2011, 7, 561. 9. Himeno, H.; Kurita, D.; Muto, A., tmRNA-mediated trans-translation as the major ribosome rescue system in a bacterial cell. Frontiers in genetics 2014, 5, 66. 10. Janssen, B. D.; Hayes, C. S., THE tmRNA RIBOSOME-RESCUE SYSTEM. Adv Protein Chem Str 2012, 86, 151-191. 11. Cameron, D. E.; Bashor, C. J.; Collins, J. J., A brief history of synthetic biology. Nat Rev Microbiol 2014, 12 (5), 381-390. 12. Stricker, J.; Cookson, S.; Bennett, M. R.; Mather, W. H.; Tsimring, L. S.; Hasty, J., A fast, robust and tunable synthetic gene oscillator. Nature 2008, 456 (7221), 516-9.


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36. Danino, T.; Mondragon-Palomino, O.; Tsimring, L.; Hasty, J., A synchronized quorum of genetic clocks. Nature 2010, 463 (7279), 326-30. 37. Prindle, A.; Samayoa, P.; Razinkov, I.; Danino, T.; Tsimring, L. S.; Hasty, J., A sensing array of radically coupled genetic 'biopixels'. Nature 2012, 481 (7379), 39-44. 38. Prindle, A.; Selimkhanov, J.; Li, H.; Razinkov, I.; Tsimring, L. S.; Hasty, J., Rapid and tunable post-translational coupling of genetic circuits. Nature 2014, 508 (7496), 387-91. 39. Cameron, D. E.; Collins, J. J., Tunable protein degradation in bacteria. Nature biotechnology 2014, 32 (12), 1276-81. 40. Schindelin, J.; Arganda-Carreras, I.; Frise, E.; Kaynig, V.; Longair, M.; Pietzsch, T.; Preibisch, S.; Rueden, C.; Saalfeld, S.; Schmid, B.; Tinevez, J. Y.; White, D. J.; Hartenstein, V.; Eliceiri, K.; Tomancak, P.; Cardona, A., Fiji: an open-source platform for biological-image analysis. Nature methods 2012, 9 (7), 676-82. 41. Wickner, S.; Gottesman, S.; Skowyra, D.; Hoskins, J.; McKenney, K.; Maurizi, M. R., A molecular chaperone, ClpA, functions like DnaK and DnaJ. Proceedings of the National Academy of Sciences of the United States of America 1994, 91 (25), 12218-22. 42. Hoskins, J. R.; Kim, S. Y.; Wickner, S., Substrate recognition by the ClpA chaperone component of ClpAP protease. The Journal of biological chemistry 2000, 275 (45), 35361-7. 43. Hoskins, J. R.; Singh, S. K.; Maurizi, M. R.; Wickner, S., Protein binding and unfolding by the chaperone ClpA and degradation by the protease ClpAP. Proceedings of the National Academy of Sciences of the United States of America 2000, 97 (16), 8892-7. 44. Hoskins, J. R.; Yanagihara, K.; Mizuuchi, K.; Wickner, S., ClpAP and ClpXP degrade proteins with tags located in the interior of the primary sequence. Proceedings of the National Academy of Sciences of the United States of America 2002, 99 (17), 11037-42. 45. Hoskins, J. R.; Wickner, S., Two peptide sequences can function cooperatively to facilitate binding and unfolding by ClpA and degradation by ClpAP. Proceedings of the National Academy of Sciences of the United States of America 2006, 103 (4), 909-14. 46. Griffith, K. L.; Shah, I. M.; Wolf, R. E., Jr., Proteolytic degradation of Escherichia coli transcription activators SoxS and MarA as the mechanism for reversing the induction of the superoxide (SoxRS) and multiple antibiotic resistance (Mar) regulons. Molecular microbiology 2004, 51 (6), 1801-16. 47. Tsilibaris, V.; Maenhaut-Michel, G.; Van Melderen, L., Biological roles of the Lon ATPdependent protease. Research in microbiology 2006, 157 (8), 701-13. 48. Gerdes, K.; Maisonneuve, E., Bacterial persistence and toxin-antitoxin loci. Annual review of microbiology 2012, 66, 103-23. 49. Hansen, S.; Vulic, M.; Min, J.; Yen, T. J.; Schumacher, M. A.; Brennan, R. G.; Lewis, K., Regulation of the Escherichia coli HipBA toxin-antitoxin system by proteolysis. PloS one 2012, 7 (6), e39185. 50. Schumacher, M. A.; Piro, K. M.; Xu, W.; Hansen, S.; Lewis, K.; Brennan, R. G., Molecular mechanisms of HipA-mediated multidrug tolerance and its neutralization by HipB. Science 2009, 323 (5912), 396-401. 51. Volkert, M. R.; Landini, P., Transcriptional responses to DNA damage. Curr Opin Microbiol 2001, 4 (2), 178-85. 52. Alekshun, M. N.; Levy, S. B., The mar regulon: multiple resistance to antibiotics and other toxic chemicals. Trends Microbiol 1999, 7 (10), 410-3. 53. Wood, T. K.; Knabel, S. J.; Kwan, B. W., Bacterial persister cell formation and dormancy. Applied and environmental microbiology 2013, 79 (23), 7116-21. 54. Maisonneuve, E.; Gerdes, K., Molecular mechanisms underlying bacterial persisters. Cell 2014, 157 (3), 539-48. 55. del Solar, G.; Giraldo, R.; Ruiz-Echevarria, M. J.; Espinosa, M.; Diaz-Orejas, R., Replication and control of circular bacterial plasmids. Microbiol Mol Biol Rev 1998, 62 (2), 43464.


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Crosstalk between Diverse Synthetic Protein ... - ACS Publications

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